
import pandas as pd
import matplotlib as mpl
import matplotlib.pyplot as plt
import seaborn as sns
import numpy as np
import geopandas
import matplotlib.ticker as ticker
import plotly.graph_objects as go
import folium
from scipy.stats import linregress
from shapely.geometry import Point, Polygon


df_upt=pd.read_csv ('C:/Users/javie/Universidad/Trimestre 2/Visualización de datos/Proyecto Visualización/Raw-Uptime/raw.uptime.2022.csv',sep=",")
df_upt.head(15)


df_cor=pd.read_excel ('C:/Users/javie/Universidad/Trimestre 2/Visualización de datos/Proyecto Visualización/Raw-Uptime/Coordenadas Sitios Moviles.xlsx','ALL')
df_cor.head(15)


df_upco = pd.merge(df_upt, df_cor, on='sitio')
df_upco.head(15)


df_upco=df_upco.drop(['Celda','TEC','Vendor/RNC'], axis=1)
df_upco.head(15)


gdf = geopandas.GeoDataFrame(
    df_upco, geometry=geopandas.points_from_xy(df_upco.Longitud, df_upco.Latitud))

print(gdf.head())

        fecha  semana  sitio            nombre       comuna tecnologia    avg  \
0  2022-01-01    52.0  11SEP  11 DE SEPTIEMBRE  Providencia         3G  100.0   
1  2022-01-01    52.0  11SEP  11 DE SEPTIEMBRE  Providencia         3G  100.0   
2  2022-01-01    52.0  11SEP  11 DE SEPTIEMBRE  Providencia         3G  100.0   
3  2022-01-01    52.0  11SEP  11 DE SEPTIEMBRE  Providencia         3G  100.0   
4  2022-01-01    52.0  11SEP  11 DE SEPTIEMBRE  Providencia         3G  100.0   

    Latitud  Longitud                     geometry  
0 -70.61111 -33.42227  POINT (-33.42227 -70.61111)  
1 -70.61111 -33.42227  POINT (-33.42227 -70.61111)  
2 -70.61111 -33.42227  POINT (-33.42227 -70.61111)  
3 -70.61111 -33.42227  POINT (-33.42227 -70.61111)  
4 -70.61111 -33.42227  POINT (-33.42227 -70.61111)


gdf.info()

<class 'geopandas.geodataframe.GeoDataFrame'>
Int64Index: 4680259 entries, 0 to 4680258
Data columns (total 10 columns):
 #   Column      Dtype   
---  ------      -----   
 0   fecha       object  
 1   semana      float64 
 2   sitio       object  
 3   nombre      object  
 4   comuna      object  
 5   tecnologia  object  
 6   avg         float64 
 7   Latitud     float64 
 8   Longitud    float64 
 9   geometry    geometry
dtypes: float64(4), geometry(1), object(5)
memory usage: 392.8+ MB


lista_AM = [i.split(" ")[0][:-3] for i in  list(gdf['fecha'])]
lista_A = [i.split(" ")[0][0:4] for i in  list(gdf['fecha'])]
lista_M = [i.split(" ")[0][5:7] for i in  list(gdf['fecha'])]
lista_D = [i.split(" ")[0][8:10] for i in  list(gdf['fecha'])]
gdf['AM'] = lista_AM
gdf['año'] = lista_A
gdf['mes'] = lista_M
gdf['dia'] = lista_D

gdf.head(15)


gdf_monthly = gdf.groupby(['comuna', 'AM', 'año', 'mes']).mean()
gdf_monthly = gdf_monthly[['avg']].reset_index()

gdf_monthly.head(30)

C:\Users\javie\AppData\Local\Temp\ipykernel_16404\4197382047.py:2: FutureWarning: The default value of numeric_only in DataFrameGroupBy.mean is deprecated. In a future version, numeric_only will default to False. Either specify numeric_only or select only columns which should be valid for the function.
  gdf_monthly = gdf.groupby(['comuna', 'AM', 'año', 'mes']).mean()


sns.set_style('darkgrid')
sns.set(rc={'figure.figsize':(6,4)})

ax = sns.lineplot(data=gdf_monthly, x ='AM', y = 'avg',
                  lw=3, legend='brief')

ax.xaxis.set_major_locator(ticker.MultipleLocator(2))
ax.grid(False)
plt.ylabel('Uptime 3G (puntos porcentuales)')
plt.xlabel('Año-Mes')
plt.savefig("uptime.png", transparent=True)
plt.show()


import plotly.graph_objects as go
comunas= gdf_monthly.comuna
comunas= np.unique(comunas).tolist()
comunas

pal = list(sns.color_palette(palette='viridis', n_colors=len(comunas)).as_hex())

fig = go.Figure()
for d,p in zip(comunas, pal):
    fig.add_trace(go.Scatter(x = gdf_monthly[gdf_monthly['comuna']==d]['AM'],
                             y = gdf_monthly[gdf_monthly['comuna']==d]['avg'],
                             name = d,
                             line_color = p, 
                             fill=None))  

fig.show()


fig = go.Figure()
for d,p in zip(comunas, pal):
    fig.add_trace(go.Scatter(x = gdf_monthly[gdf_monthly['comuna']==d]['AM'],
                             y = gdf_monthly[gdf_monthly['comuna']==d]['avg'],
                             name = d,
                             line_color = p, 
                             fill='tozeroy'))    

fig.show()


g = sns.relplot(data = gdf_monthly, x = "AM", y = "avg",
                col = "comuna", hue = "comuna",
                kind = "line", palette = "Spectral",   
                linewidth = 4, zorder = 5,
                col_wrap = 5, height = 3, aspect = 1.5, legend = False
               )


for time, ax in g.axes_dict.items():
    ax.text(.1, .85, time,
            transform = ax.transAxes, fontweight="bold"
           )
    sns.lineplot(data = gdf_monthly, x = "AM", y = "avg", units="comuna",
                 estimator = None, color= ".7", linewidth=1, ax=ax
                )

ax.set_xticks('')
g.set_titles("")
g.set_axis_labels("", "Uptime 3G")
g.tight_layout()

<seaborn.axisgrid.FacetGrid at 0x1da93881390>


import warnings
warnings.simplefilter(action='ignore', category=FutureWarning)

g = sns.FacetGrid(gdf_monthly, col="año", row="mes", height=2, aspect=1.9)
g = g.map(sns.barplot, 'comuna', 'avg', palette='viridis', errorbar=None, order = comunas)

g.set_xticklabels(rotation = 90)
plt.show()


df_pivot = pd.pivot_table(gdf_monthly,
                          values='avg',
                          index='comuna',
                          columns='AM')
df_pivot


plt.figure(figsize = (8,15))
plt.title('Uptime promedio por comuna en pp')

sns.heatmap(df_pivot, annot=True, cmap='RdYlBu', fmt= '.4g',)
#plt.xlabel('Año-Mes')
#plt.ylabel('Comuna')
plt.show()


import plotly.graph_objects as go


pal = list(sns.color_palette(palette='viridis', n_colors=len(comunas)).as_hex())

months = list(reversed([str(i) for i in list(range(1,10))])) + ['9']
list_PM = [[list(gdf_monthly[gdf_monthly['comuna']==i]['avg'])[int(n)-1] for n in months] for i in comunas]

fig = go.Figure()
for pm,d,c in zip(list_PM, comunas, pal):
    fig.add_trace(go.Scatterpolar(r = pm, theta=months, fill= None,
                                  name=str(d), marker = dict(color = c)))

fig.update_layout(polar = dict(radialaxis = dict(visible = True, range=[0, 100]),
                               angularaxis = dict(rotation=90)),
                  showlegend=True, width=720, height=720,
                  font = dict(size=14))

fig.show()


keep_figname = []
for i in comunas:
    fig = go.Figure()
    for d, pm, c in zip(comunas, list_PM, pal):
        
        
        if i == d:
            fig.add_trace(go.Scatterpolar(r = pm, theta=months, fill='toself',
                                          name=str(d), marker = dict(color = c)))
        else:
            fig.add_trace(go.Scatterpolar(r = pm, theta=months, fill= None,
                                          name=str(d), marker = dict(color = c)))
   
    fig.update_layout(polar = dict(radialaxis = dict(visible = True, range=[0, 100]),
                                   angularaxis = dict(rotation=90)),
                      showlegend = True, width = 720, height = 720,
                      font = dict(size = 14), title = i, title_x = 0.5)
    
    keep_figname.append('radar_'+ i + '.png')
    fig.write_image('radar_'+ i + '.png')   
    fig.show()


max_val = max(gdf_monthly['avg'])*1.001       
pal = list(sns.color_palette(palette='YlOrRd', n_colors=len(comunas)).as_hex())

def circular_bar(input_df, column_name, title):
    plt.gcf().set_size_inches(12, 12)
    ax = plt.subplot(projection='polar')
    input_df.reset_index(inplace=True, drop=True)
    for i in range(len(input_df)):
        ax.barh(i, input_df[column_name][i]*2*np.pi/max_val, label=input_df['comuna'][i], color=pal[i])

    ax.set_theta_zero_location('N')
    ax.set_theta_direction(1)
    ax.set_rlabel_position(0)
    ax.set_thetagrids([], labels=[])
    ax.set_rgrids(range(len(input_df)), labels= input_df['comuna'])

    ax = plt.subplot(projection='polar')
    plt.title("Average Uptime // " + title)
    return ax


list_month19 = list(set(gdf_monthly['mes']))
list_YM19 = list(set(gdf_monthly['AM']))
list_YM19.sort()
listdf_monthly19 = [gdf_monthly[gdf_monthly['mes']==str(i)] for i in list_month19]

keep_sname = []
order = range(len(listdf_monthly19))

circular_bar(listdf_monthly19[i],'avg', list_YM19[i])
keep_sname.append('cir_bar_' + str(i) + '.png')
plt.savefig('cir_bar_' + str(i) + '.png')
plt.show()


pal = list(sns.color_palette(palette='mako', n_colors=len(comunas)).as_hex())
lowerLimit = 0
max_v = gdf_monthly['avg'].max()

def radial_plot(input_df, column_name, title):
    input_df.reset_index(inplace=True, drop=True)
    plt.figure(figsize=(12,12))
    ax = plt.subplot(111, polar=True)
    plt.axis()
    
    heights = input_df[column_name]
    width = 2*np.pi / len(input_df.index)

    indexes = list(range(1, len(input_df.index)+1))
    angles = [element * width for element in indexes]

    bars = ax.bar(x=angles, height=heights, width=width, bottom=lowerLimit,
                  linewidth=1, edgecolor="white", color=pal)
    
    labelPadding = 2

    for bar, angle, height, label in zip(bars, angles, heights, comunas):
        rotation = np.rad2deg(angle)
        alignment = ""
      
        if angle >= np.pi/2 and angle < 3*np.pi/2:
            alignment = "right"
            rotation = rotation + 180
        else: 
            alignment = "left"

        ax.text(x=angle, y=lowerLimit + bar.get_height() + labelPadding,
                s=label, ha=alignment, va='center', rotation=rotation, 
                rotation_mode="anchor")
    
        ax.set_thetagrids([], labels=[])
        
    return ax


keep_sname = []
order = range(len(listdf_monthly19))

#for i in order:
radial_plot(listdf_monthly19[i], 'avg', list_YM19[i])
keep_sname.append('rad_bar_' + str(i) + '.png')
plt.savefig('rad_bar_' + str(i) + '.png',transparent=True)
plt.show()


pal = list(sns.color_palette(palette='viridis', n_colors=len(list_month19)).as_hex())

def kde_ridge(df_input, col_name, time, title):
    sns.set_theme(style="white", rc={"axes.facecolor": (0, 0, 0, 0)})
    
    g = sns.FacetGrid(df_input, row= time, hue=time, aspect=15, height=0.65, palette=pal)

    
    g.map(sns.kdeplot, col_name,
          bw_adjust=.5, clip_on=False,fill=True, alpha=1, linewidth=1.5)

    g.map(sns.kdeplot, col_name, clip_on=False, color="w", lw=2, bw_adjust=.5)

    
    g.map(plt.axhline, y=0, linewidth=2, linestyle="-", color=None, clip_on=False)
    
    def label(x, color, label):
        ax = plt.gca()
        ax.text(0, .2, label, fontweight="bold", color=color,ha="left", va="center", transform=ax.transAxes)
    
    g.map(label, time)

    
    g.fig.subplots_adjust(hspace=-.25)

    
    g.set_titles("")
    g.set(yticks=[], ylabel="", xlabel= title + ' Uptime')
    g.despine(bottom=True, left=True)

    return g


df_hd19 = [gdf_day[gdf_day['comuna']==i] for i in comunas]

keep_sname = []
order = range(len(comunas))
for i in order:
    kde_ridge(df_hd19[i], 'avg', 'AM', comunas[i])
    keep_sname.append('kde_' + str(i) + '.png')
    plt.savefig('kde_' + str(i) + '.png', transparent=True)
    plt.show()


gdf_day = gdf.groupby(['comuna', 'AM', 'año', 'mes','dia']).mean()
gdf_day = gdf_day[['avg']].reset_index()

gdf_day.head(30)


sf_path = "C:/Users/javie/Universidad/Trimestre 2/Visualización de datos/Proyecto Visualización/L/lu.shp"
sf = geopandas.read_file(sf_path, encoding='utf-8')
sf.head(100)
stgo_sf = sf[sf.REGION == '13']
stgo_shape = stgo_sf.to_crs({'init': 'epsg:4326'})
stgo_shape


df_sitios = gdf.groupby(['comuna', 'sitio', 'Latitud','Longitud']).mean()
df_sitios = df_sitios[['avg']].reset_index()

df_sitios = geopandas.GeoDataFrame(
    df_sitios, geometry=geopandas.points_from_xy(df_sitios.Latitud, df_sitios.Longitud))


fig, ax = plt.subplots(1,2, figsize=(14,8))

df_sitios.plot(ax=ax[0])
stgo_shape.plot(ax=ax[1])

plt.show()


import matplotlib.pyplot as plt

fig, ax = plt.subplots()

ax.set_aspect('equal')

stgo_shape.plot(ax=ax, color='white', edgecolor='black')
 

df_sitios.plot(ax=ax, marker='o', color='red', markersize=5)


plt.show();


import folium


m = folium.Map(location=[20,0], tiles="OpenStreetMap", zoom_start=2)
for i in range(0,len(df_sitios)):
   folium.Marker(
      location=[df_sitios.iloc[i]['Longitud'], df_sitios.iloc[i]['Latitud']],
      popup=df_sitios.iloc[i]['sitio'],
   ).add_to(m)


m


import geoplot
import geoplot.crs as gcrs



ax = geoplot.kdeplot(df_sitios, cmap='magma_r', shade=True,
                     projection=gcrs.AlbersEqualArea())

geoplot.polyplot(stgo_shape, ax=ax)

<GeoAxesSubplot: >


df_delito=pd.read_excel ('C:/Users/javie/Universidad/Trimestre 2/Visualización de datos/Proyecto Visualización/delitos.xlsx')
df_delito.head(15)
df_delito.info()

<class 'pandas.core.frame.DataFrame'>
RangeIndex: 312 entries, 0 to 311
Data columns (total 3 columns):
 #   Column   Non-Null Count  Dtype         
---  ------   --------------  -----         
 0   fecha    312 non-null    datetime64[ns]
 1   comuna   312 non-null    object        
 2   delitos  312 non-null    int64         
dtypes: datetime64[ns](1), int64(1), object(1)
memory usage: 7.4+ KB


df_delito['fecha'] = df_delito['fecha'].dt.strftime('%Y-%m-%d')


df_delito.info()

<class 'pandas.core.frame.DataFrame'>
RangeIndex: 312 entries, 0 to 311
Data columns (total 3 columns):
 #   Column   Non-Null Count  Dtype 
---  ------   --------------  ----- 
 0   fecha    312 non-null    object
 1   comuna   312 non-null    object
 2   delitos  312 non-null    int64 
dtypes: int64(1), object(2)
memory usage: 7.4+ KB


lista_AM1 = [i.split(" ")[0][:-3] for i in  list(df_delito['fecha'])]
df_delito['AM'] = lista_AM1
del df_delito['fecha']
df_delito


df45=df_delito.groupby(['comuna']).mean()
df45=df45.reset_index()

df45.sort_values(by='delitos', ascending=False)


df_topdelito = df_delito[df_delito.comuna.isin(['Santiago', 'San Bernardo', 'Puente Alto', 'La Florida','Maipú'])]
df_topdelito


stgo_shape['color'] = ['1' if (x=='13401') or (x=='13101') or (x=='13110') or (x=='13201') or (x=='13119') else 'Otros' for x in stgo_shape.COMUNA]

stgo_shape.head(100)


df_topdelito2 = df_topdelito.groupby(['AM']).mean()
df_topdelito2 = df_topdelito2['delitos'].reset_index()
df_topdelito2


df_final = pd.merge(gdf_monthly, df_delito, on=['AM','comuna'])
df_final.head(15)
del df_final['año']
del df_final['mes']
df_final


df_final2 = df_final.groupby(['AM']).mean()
df_final2 = df_final2.reset_index()
df_final2


df_final3 = df_final2[df_final2.AM.isin(['2022-01','2022-02','2022-03', '2022-04', '2022-05', '2022-06'])]
df_final3


sns.set_style('darkgrid')
sns.set(rc={'figure.figsize':(14,8)})

ax = sns.lineplot(data=df_topdelito2, x ='AM', y = 'delitos',
                 palette='viridis',
                  legend='full', lw=3)

ax.xaxis.set_major_locator(ticker.MultipleLocator(1))
plt.legend(bbox_to_anchor=(1, 1))
plt.ylabel('Delitos')
plt.xlabel('Año-Mes')
plt.show()

C:\Users\javie\AppData\Local\Temp\ipykernel_16404\2943057135.py:4: UserWarning:

Ignoring `palette` because no `hue` variable has been assigned.

No artists with labels found to put in legend.  Note that artists whose label start with an underscore are ignored when legend() is called with no argument.
C:\Users\javie\AppData\Roaming\Python\Python310\site-packages\IPython\core\pylabtools.py:151: UserWarning:

This figure includes Axes that are not compatible with tight_layout, so results might be incorrect.


sns.lineplot(data=df_final2)

<AxesSubplot: >


df_uptime_top = gdf_monthly[gdf_monthly.comuna.isin(['Santiago', 'San Bernardo', 'Puente Alto', 'La Florida','Maipú'])]
df_uptime_top


sns.set_style('darkgrid')
sns.set(rc={'figure.figsize':(14,8)})

ax = sns.lineplot(data=df_uptime_top, x ='AM', y = 'avg',
                  lw=3, legend='brief')

ax.xaxis.set_major_locator(ticker.MultipleLocator(2))

plt.ylabel('Uptime 3G (puntos porcentuales)')
plt.xlabel('Año-Mes')
plt.show()


from matplotlib.lines import Line2D
    
g = sns.lineplot(data=df_final3.avg, color="g")
sns.lineplot(data=df_final3.delitos, color="b", ax=g.axes.twinx())
g.legend(handles=[Line2D([], [], color="g", label='Uptime'), Line2D([], [], color="b", label='Hechos delictivos')])

<matplotlib.legend.Legend at 0x1db3e474280>


f = plt.figure(figsize=(10, 8))
plt.matshow(df_final.corr(), fignum=f.number)
plt.xticks(range(df_final.select_dtypes(['number']).shape[1]), df_final.select_dtypes(['number']).columns, fontsize=14, rotation=45)
plt.yticks(range(df_final.select_dtypes(['number']).shape[1]), df_final.select_dtypes(['number']).columns, fontsize=14)
cb = plt.colorbar()
cb.ax.tick_params(labelsize=14)
plt.title('Correlation Matrix', fontsize=16);

C:\Users\javie\AppData\Roaming\Python\Python310\site-packages\IPython\core\events.py:89: UserWarning:

This figure includes Axes that are not compatible with tight_layout, so results might be incorrect.


import plotly.graph_objects as go
comunas2= df_topdelito.comuna
comunas2= np.unique(comunas2).tolist()
comunas2

pal = list(sns.color_palette(palette='viridis', n_colors=len(comunas2)).as_hex())

months = list(reversed([str(i) for i in list(range(1,7))])) + ['6']
list_PM = [[list(df_topdelito[df_topdelito['comuna']==i]['delitos'])[int(n)-1] for n in months] for i in comunas2]

fig = go.Figure()
for pm,d,c in zip(list_PM, comunas2, pal):
    fig.add_trace(go.Scatterpolar(r = pm, theta=months, fill= None,
                                  name=str(d), marker = dict(color = c)))

fig.update_layout(polar = dict(radialaxis = dict(visible = True, range=[1000, 4000]),
                               angularaxis = dict(rotation=90)),
                  showlegend=True, width=600, height=500,
                  font = dict(size=14))
fig.write_image('robo.png')
fig.show()


df_delito.plot()

<AxesSubplot: >


import plotly.graph_objects as go
comunas3= df_delito.comuna
comunas3= np.unique(comunas3).tolist()
comunas3

pal = list(sns.color_palette(palette='viridis', n_colors=len(comunas3)).as_hex())

months = list(reversed([str(i) for i in list(range(1,7))])) + ['6']
list_PM = [[list(df_delito[df_delito['comuna']==i]['delitos'])[int(n)-1] for n in months] for i in comunas3]

fig = go.Figure()
for pm,d,c in zip(list_PM, comunas3, pal):
    fig.add_trace(go.Scatterpolar(r = pm, theta=months, fill= None,
                                  name=str(d), marker = dict(color = c)))

fig.update_layout(polar = dict(radialaxis = dict(visible = True, range=[0, 4000]),
                               angularaxis = dict(rotation=90)),
                  showlegend=True, width=720, height=720,
                  font = dict(size=14))
fig.write_image('radar_'+ i + '.png',transparent=True)
fig.show()

<Figure size 800x600 with 0 Axes>


ax=sns.regplot(x = "avg", y="delitos", data=df_final, fit_reg = False, scatter_kws={"alpha": 0.2})

sns.set(rc={'figure.figsize':(7,5),'legend.frameon':False} )

sns.axes_style({'text.color': 'white','ytick.color': 'white','axes.labelcolor': 'white'})


mini=[0]
maxi=[100]
maxi1=[4000]
ax.xaxis.set_major_locator(ticker.MultipleLocator(10))
#ax.grid(False)
#plt.box(on=None)

plt.ylabel('Hechos delictuales')
plt.xlabel('uptime')
plt.xlim(min(mini),max(maxi))
plt.ylim(min(mini),max(maxi1))
plt.xticks(np.linspace(0, 100, 2))  # arbitrary chosen
plt.yticks(np.linspace(0,4000, 2))
plt.savefig("relacion2.png", transparent=True)
plt.show()


ax=sns.jointplot(x = "avg", y="delitos", data=df_final, kind = 'kde')
sns.set_style('darkgrid')
sns.set(rc={'figure.figsize':(6,4)})

mini=[0]
maxi=[100]
maxi1=[4000]

plt.ylabel('Hechos delictuales')
plt.xlabel('Uptime')
plt.savefig("relacion1.png", transparent=True)
plt.xlim(min(mini),max(maxi))
plt.ylim(min(mini),max(maxi1))
plt.xticks(np.linspace(0, 100, 2))  
plt.yticks(np.linspace(0,4000, 2))
plt.show()


sns.FacetGrid(df_final, col="comuna").map(plt.scatter, "avg", "delitos", alpha = 1).add_legend()


import numpy as np
import matplotlib.pyplot as plt

x = df_sitios['Latitud'].ravel()
y = df_sitios['Longitud'].ravel()
c = df_sitios['avg'].ravel()
ax.grid(False)
plt.box(on=None)

COLOR = 'white'
mpl.rcParams['text.color'] = 'black'
mpl.rcParams['axes.labelcolor'] = 'black'
mpl.rcParams['xtick.color'] = 'black'
mpl.rcParams['ytick.color'] = 'black'

plt.rcParams["figure.figsize"] = (10,7)
plt.scatter(x, y, c=c, s=200)
plt.colorbar()
plt.savefig("heatmap.png", transparent=True)
plt.show()


stgo_shape.explore(column='color', cmap=['red', 'blue'])


df_sitios2=df_sitios
df_sitios2


df_sitios2['delito']= df45.comuna.map(df45.set_index(df45.comuna)['delitos'])
df_sitios2

	sitio	TEC	Vendor/RNC	Celda	Latitud	Longitud
0	TTNF2	3G	UATF1-50	TTNF2U02_2-1	-70.39379	-23.64867
1	TTNF2	3G	UATF1-50	TTNF2U02_8-2	-70.39379	-23.64867
2	ESNO2	3G	UATF1-50	ESNO2U02_1-1	-69.05169	-24.20289
3	ESNO2	3G	UATF1-50	ESNO2U02_7-2	-69.05169	-24.20289
4	ESNO2	3G	UATF1-50	ESNO2U02_2-1	-69.05169	-24.20289
5	ESNO2	3G	UATF1-50	ESNO2U02_8-2	-69.05169	-24.20289
6	ESNO2	3G	UATF1-50	ESNO2U02_3-1	-69.05169	-24.20289
7	ESNO2	3G	UATF1-50	ESNO2U02_9-2	-69.05169	-24.20289
8	TTNF2	3G	UATF1-50	TTNF2U02_1-1	-70.39379	-23.64867
9	TTNF2	3G	UATF1-50	TTNF2U02_3-1	-70.39379	-23.64867
10	TTNF2	3G	UATF1-50	TTNF2U02_7-2	-70.39379	-23.64867
11	TTNF2	3G	UATF1-50	TTNF2U02_9-2	-70.39379	-23.64867
12	B7101	3G	UATF1-50	B7101U02_1-1	-70.49867	-25.09109
13	B7105	3G	UATF1-50	B7105U02_1-1	-70.31022	-24.37583
14	B7105	3G	UATF1-50	B7105U02_2-1	-70.31022	-24.37583

AM	2022-01	2022-02	2022-03	2022-04	2022-05	2022-06	2022-07	2022-08	2022-09
comuna
Alhué	96.221935	98.358095	98.336022	95.085167	99.994946	95.579611	99.631667	99.870376	99.940800
Buin	86.969989	76.286458	80.706100	93.295152	94.797520	95.543655	92.739940	97.267675	99.464496
Calera de Tango	77.503364	61.248653	63.495775	82.148590	91.139744	96.615526	92.587973	92.229715	98.041777
Cerrillos	99.046030	89.873320	86.588778	86.768078	87.299430	82.125505	92.837436	93.149499	93.121506
Cerro Navia	79.581854	58.924787	62.823312	91.606540	81.583335	91.864231	82.943684	97.287016	93.645559
Colina	85.909678	81.019276	93.670485	85.256127	95.470543	96.138343	91.305378	95.291379	94.845511
Conchalí	88.793201	61.730479	68.374663	78.336421	93.295643	96.848822	98.753782	99.504983	99.099783
Curacaví	94.810840	85.080177	97.736343	90.166748	96.807091	89.201203	90.110328	98.497370	97.060013
El Bosque	79.108942	56.931238	54.861879	87.889409	91.526304	89.295109	91.303947	88.822175	97.364062
El Monte	47.165712	37.016179	49.131556	87.757914	80.349586	89.592581	95.484341	91.544102	99.090390
Estación Central	94.836588	91.166035	86.815609	92.342193	92.595055	91.184058	92.001140	91.054259	95.163252
Huechuraba	88.327844	88.406954	92.397528	87.650878	95.890413	97.919367	94.278826	96.525139	97.983563
Independencia	94.118684	96.412783	93.806448	92.653407	99.720131	99.926983	99.100077	97.873239	90.297318
Isla de Maipo	96.202929	82.792136	75.449795	92.527883	96.472794	96.172604	90.875794	99.543468	91.623785
La Cisterna	73.794164	65.168459	76.946373	85.272903	93.963514	88.628242	92.951181	96.230463	95.008474
La Florida	90.651512	87.234649	92.828525	92.435074	96.214927	96.417638	98.044541	98.204428	98.361645
La Granja	46.145154	18.561207	14.838094	82.131116	83.288325	79.157125	92.287652	96.525957	97.496886
La Pintana	47.000730	61.218742	53.503730	71.022337	67.883594	83.385411	85.531600	83.921613	91.558847
La Reina	93.777198	92.341305	97.069266	98.115989	97.154929	98.136087	95.090168	97.414881	98.936644
Lampa	91.921830	86.740102	85.453458	95.093385	97.380006	95.888342	95.420932	96.812310	96.314191
Las Condes	96.552077	95.566425	95.053894	97.655697	97.238655	98.259995	96.724975	98.281266	97.508695
Lo Barnechea	98.868166	96.093099	94.195787	95.096377	95.683208	97.933744	95.934941	99.371836	99.759907
Lo Espejo	73.270086	55.969879	69.501767	94.987859	88.351533	91.948694	91.774526	94.521837	92.143880
Lo Prado	95.691355	93.317840	95.334547	98.461082	90.019389	90.176502	87.103684	95.730406	98.826673
Macul	91.616677	90.010623	91.331656	98.044613	99.072599	96.770830	97.464569	97.124690	97.867699
Maipú	82.627683	75.200740	77.768713	82.538197	85.825860	92.109876	91.225154	91.218716	92.274905
María Pinto	99.999376	99.990125	99.999446	99.475422	99.267527	99.908311	99.998269	92.905500	99.509940
Melipilla	93.274544	87.813098	89.136816	97.216186	98.918179	95.389785	93.253691	98.520286	98.528359
Padre Hurtado	90.188133	79.740462	75.690344	93.538160	91.172505	97.261770	92.073412	97.397581	94.521917
Paine	94.732755	87.859756	93.528963	97.584963	92.741378	97.393270	95.650252	98.216701	99.426442
Pedro Aguirre Cerda	77.701489	53.511157	52.306900	80.635654	93.120213	93.012879	97.987970	98.126293	96.655322
Peñaflor	79.246138	71.349824	74.476139	86.109866	89.561914	96.973130	97.827879	96.512740	97.537886
Peñalolén	94.823721	89.892672	89.797235	97.381377	97.495628	98.507465	98.299275	98.532321	93.556373
Pirque	97.862805	61.394125	75.695557	88.535677	99.419052	96.846350	94.564700	97.858356	97.818535
Providencia	98.237961	95.057165	97.590877	97.821933	98.338947	99.346692	99.548343	99.257484	99.011882
Pudahuel	89.949864	80.791383	72.594079	86.545545	89.957843	92.879479	92.292749	98.591874	97.848854
Puente Alto	87.287262	77.387800	86.394099	87.713319	91.656416	93.989090	94.457920	94.379073	97.139334
Quilicura	88.309197	81.537234	83.117029	86.956908	93.212559	94.680270	96.176092	97.379557	97.484427
Quinta Normal	89.024598	89.848358	89.574515	98.405714	91.134397	98.384745	95.870721	96.100005	99.237009
Recoleta	89.404915	90.984069	90.724884	90.102072	95.121737	98.366531	92.687276	95.829297	96.731982
Renca	85.402103	85.415037	67.846733	77.806088	79.148416	91.050552	92.350456	87.915021	86.786032
San Bernardo	82.700206	71.428512	61.050954	80.037050	89.173343	87.223222	87.874210	92.339677	95.188521
San Joaquín	92.286145	79.284906	91.832438	90.467677	96.790878	99.280938	97.511861	94.578779	96.116781
San José de Maipo	88.410925	89.868092	71.261809	93.536682	94.485998	90.459786	82.553420	94.569297	97.349650
San Miguel	93.907554	86.219298	81.397368	87.128884	90.627621	96.166327	92.441891	94.039351	96.910308
San Pedro	98.414214	98.283802	99.331364	97.354903	99.195356	93.472472	91.488696	90.763817	97.869242
San Ramón	62.337482	42.166104	36.786081	81.619002	88.843809	91.290641	94.836073	91.539660	97.059427
Santiago	95.613784	94.233562	92.563308	94.256575	97.473375	98.296878	96.657851	96.701927	95.861962
Talagante	92.315902	85.201602	90.400421	94.936534	97.050237	91.682275	95.107773	98.150112	95.230891
Tiltil	76.519574	56.577886	87.808317	86.626467	90.651145	78.071170	91.039816	99.223946	98.330307
Vitacura	95.457149	94.957792	98.097847	98.426002	96.146331	98.311873	97.542801	98.741802	98.666568
Ñuñoa	88.163816	91.395610	88.274459	96.262745	96.722523	96.947660	97.389481	97.425183	98.001653

	FID	REGION	NOM_REGION	PROVINCIA	NOM_PROVIN	COMUNA	NOM_COMUNA	URBANO	TIPO	CODIGO_CAT	CATEGORIA	Shape__Are	Shape__Len	geometry
121	122	13	REGIÓN METROPOLITANA DE SANTIAGO	134	MAIPO	13404	PAINE	PAINE	CAPITAL COMUNAL	1	CIUDAD	7.914834e+06	17793.646475	POLYGON ((-70.73797 -33.79851, -70.73738 -33.7...
122	123	13	REGIÓN METROPOLITANA DE SANTIAGO	134	MAIPO	13404	PAINE	HUELQUÉN	URBANO	2	PUEBLO	3.053472e+06	20488.963662	POLYGON ((-70.64840 -33.81521, -70.64859 -33.8...
123	124	13	REGIÓN METROPOLITANA DE SANTIAGO	134	MAIPO	13404	PAINE	HOSPITAL	URBANO	1	CIUDAD	6.642026e+06	17333.663301	POLYGON ((-70.76468 -33.84349, -70.76448 -33.8...
124	125	13	REGIÓN METROPOLITANA DE SANTIAGO	134	MAIPO	13404	PAINE	PINTUÉ - LA GUACHERA	URBANO	2	PUEBLO	4.355833e+06	18179.078840	POLYGON ((-70.87824 -33.86540, -70.87839 -33.8...
125	126	13	REGIÓN METROPOLITANA DE SANTIAGO	134	MAIPO	13404	PAINE	EL TRÁNSITO	URBANO	2	PUEBLO	9.777246e+05	9511.271637	POLYGON ((-70.65834 -33.78357, -70.65832 -33.7...
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
219	220	13	REGIÓN METROPOLITANA DE SANTIAGO	136	TALAGANTE	13602	EL MONTE	EL MONTE	CAPITAL COMUNAL	1	CIUDAD	1.880755e+07	28217.161075	POLYGON ((-70.99282 -33.67260, -70.99282 -33.6...
220	221	13	REGIÓN METROPOLITANA DE SANTIAGO	136	TALAGANTE	13601	TALAGANTE	TALAGANTE	CAPITAL PROVINCIAL	1	CIUDAD	1.500422e+07	23840.504179	POLYGON ((-70.92714 -33.64912, -70.92705 -33.6...
221	222	13	REGIÓN METROPOLITANA DE SANTIAGO	136	TALAGANTE	13601	TALAGANTE	EL ROTO CHILENO	URBANO	2	PUEBLO	3.231334e+05	3611.941090	POLYGON ((-70.83494 -33.66122, -70.83443 -33.6...
222	223	13	REGIÓN METROPOLITANA DE SANTIAGO	136	TALAGANTE	13601	TALAGANTE	LONQUÉN	URBANO	2	PUEBLO	2.920640e+05	3503.678026	POLYGON ((-70.85226 -33.71130, -70.85154 -33.7...
223	224	13	REGIÓN METROPOLITANA DE SANTIAGO	135	MELIPILLA	13502	ALHUÉ	VILLA ALHUÉ	CAPITAL COMUNAL	2	PUEBLO	3.798828e+06	10101.551989	POLYGON ((-71.09853 -34.02734, -71.09753 -34.0...

	comuna	delitos
47	Santiago	3167.500000
36	Puente Alto	1578.833333
25	Maipú	1381.333333
15	La Florida	1213.666667
41	San Bernardo	1153.666667
34	Providencia	1083.666667
10	Estación Central	859.166667
39	Recoleta	831.000000
20	Las Condes	771.000000
35	Pudahuel	755.166667
51	Ñuñoa	748.166667
32	Peñalolén	662.500000
37	Quilicura	642.833333
38	Quinta Normal	547.833333
44	San Miguel	523.333333
17	La Pintana	519.666667
14	La Cisterna	474.500000
5	Colina	469.000000
40	Renca	455.500000
8	El Bosque	454.166667
27	Melipilla	448.666667
12	Independencia	425.500000
3	Cerrillos	413.333333
1	Buin	398.000000
6	Conchalí	384.333333
19	Lampa	382.333333
16	La Granja	372.833333
24	Macul	365.000000
23	Lo Prado	360.166667
4	Cerro Navia	359.500000
30	Pedro Aguirre Cerda	335.500000
11	Huechuraba	329.333333
22	Lo Espejo	326.166667
18	La Reina	310.500000
42	San Joaquín	310.333333
48	Talagante	292.833333
50	Vitacura	283.833333
46	San Ramón	272.333333
31	Peñaflor	253.000000
29	Paine	198.833333
28	Padre Hurtado	196.333333
21	Lo Barnechea	188.500000
13	Isla de Maipo	126.166667
7	Curacaví	126.000000
9	El Monte	119.333333
2	Calera de Tango	91.333333
43	San José de Maipo	75.500000
49	Tiltil	64.833333
33	Pirque	54.833333
45	San Pedro	39.666667
26	María Pinto	33.666667
0	Alhué	16.500000

	FID	REGION	NOM_REGION	PROVINCIA	NOM_PROVIN	COMUNA	NOM_COMUNA	URBANO	TIPO	CODIGO_CAT	CATEGORIA	Shape__Are	Shape__Len	geometry	color
121	122	13	REGIÓN METROPOLITANA DE SANTIAGO	134	MAIPO	13404	PAINE	PAINE	CAPITAL COMUNAL	1	CIUDAD	7.914834e+06	17793.646475	POLYGON ((-70.73797 -33.79851, -70.73738 -33.7...	Otros
122	123	13	REGIÓN METROPOLITANA DE SANTIAGO	134	MAIPO	13404	PAINE	HUELQUÉN	URBANO	2	PUEBLO	3.053472e+06	20488.963662	POLYGON ((-70.64840 -33.81521, -70.64859 -33.8...	Otros
123	124	13	REGIÓN METROPOLITANA DE SANTIAGO	134	MAIPO	13404	PAINE	HOSPITAL	URBANO	1	CIUDAD	6.642026e+06	17333.663301	POLYGON ((-70.76468 -33.84349, -70.76448 -33.8...	Otros
124	125	13	REGIÓN METROPOLITANA DE SANTIAGO	134	MAIPO	13404	PAINE	PINTUÉ - LA GUACHERA	URBANO	2	PUEBLO	4.355833e+06	18179.078840	POLYGON ((-70.87824 -33.86540, -70.87839 -33.8...	Otros
125	126	13	REGIÓN METROPOLITANA DE SANTIAGO	134	MAIPO	13404	PAINE	EL TRÁNSITO	URBANO	2	PUEBLO	9.777246e+05	9511.271637	POLYGON ((-70.65834 -33.78357, -70.65832 -33.7...	Otros
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
216	217	13	REGIÓN METROPOLITANA DE SANTIAGO	131	SANTIAGO	13130	SAN MIGUEL	SAN MIGUEL	CAPITAL COMUNAL	1	CIUDAD	1.386381e+07	16029.803899	POLYGON ((-70.64197 -33.47650, -70.64191 -33.4...	Otros
217	218	13	REGIÓN METROPOLITANA DE SANTIAGO	131	SANTIAGO	13131	SAN RAMÓN	SAN RAMÓN	CAPITAL COMUNAL	1	CIUDAD	9.061336e+06	14097.960667	POLYGON ((-70.63742 -33.51877, -70.63723 -33.5...	Otros
218	219	13	REGIÓN METROPOLITANA DE SANTIAGO	131	SANTIAGO	13101	SANTIAGO	SANTIAGO	CAPITAL REGIONAL	1	CIUDAD	3.332991e+07	29265.433463	POLYGON ((-70.66560 -33.42810, -70.66457 -33.4...	1
219	220	13	REGIÓN METROPOLITANA DE SANTIAGO	136	TALAGANTE	13602	EL MONTE	EL MONTE	CAPITAL COMUNAL	1	CIUDAD	1.880755e+07	28217.161075	POLYGON ((-70.99282 -33.67260, -70.99282 -33.6...	Otros
220	221	13	REGIÓN METROPOLITANA DE SANTIAGO	136	TALAGANTE	13601	TALAGANTE	TALAGANTE	CAPITAL PROVINCIAL	1	CIUDAD	1.500422e+07	23840.504179	POLYGON ((-70.92714 -33.64912, -70.92705 -33.6...	Otros

	fecha	semana	sitio	nombre	comuna	tecnologia	avg
0	2022-01-01	52.0	11SEP	11 DE SEPTIEMBRE	Providencia	3G	100.0
1	2022-01-01	52.0	14COS	OCHAGAVIA COSITE	El Bosque	3G	100.0
2	2022-01-01	52.0	15COS	LA ESTRELLA COSITE	Pudahuel	3G	100.0
3	2022-01-01	52.0	2NENT	DOS NORTE NEXTEL	La Granja	3G	0.0
4	2022-01-01	52.0	4CENF	CUARTO CENTENARIO	Las Condes	3G	100.0
5	2022-01-01	52.0	5ANUE	5 DE ABRIL ENTEL	Estación Central	3G	100.0
6	2022-01-01	52.0	5DEUM	5 DE ABRIL / LAS REJAS	Estación Central	3G	100.0
7	2022-01-01	52.0	5DFF2	CINCO DE FEBRERO	Cerro Navia	3G	100.0
8	2022-01-01	52.0	A2EN1	ALIMENTOS DOS EN UNO	Las Condes	3G	100.0
9	2022-01-01	52.0	AAANT	AGUAS ANDINAS / ZONA ANTILCO INDOOR	La Granja	3G	0.0
10	2022-01-01	52.0	AANAU	CRUCE SANTA ANA	Calera de Tango	3G	100.0
11	2022-01-01	52.0	AATCU	SANTA ADRIANA ENTEL	Talagante	3G	100.0
12	2022-01-01	52.0	ABARF	ALMIRANTE BARROSO	Santiago	3G	100.0
13	2022-01-01	52.0	ABEDF	LOS ABEDULES	Vitacura	3G	100.0
14	2022-01-01	52.0	ABELF	ANDRES BELLO	Providencia	3G	100.0

	comuna	AM	año	mes	avg
0	Alhué	2022-01	2022	01	96.221935
1	Alhué	2022-02	2022	02	98.358095
2	Alhué	2022-03	2022	03	98.336022
3	Alhué	2022-04	2022	04	95.085167
4	Alhué	2022-05	2022	05	99.994946
5	Alhué	2022-06	2022	06	95.579611
6	Alhué	2022-07	2022	07	99.631667
7	Alhué	2022-08	2022	08	99.870376
8	Alhué	2022-09	2022	09	99.940800
9	Buin	2022-01	2022	01	86.969989
10	Buin	2022-02	2022	02	76.286458
11	Buin	2022-03	2022	03	80.706100
12	Buin	2022-04	2022	04	93.295152
13	Buin	2022-05	2022	05	94.797520
14	Buin	2022-06	2022	06	95.543655
15	Buin	2022-07	2022	07	92.739940
16	Buin	2022-08	2022	08	97.267675
17	Buin	2022-09	2022	09	99.464496
18	Calera de Tango	2022-01	2022	01	77.503364
19	Calera de Tango	2022-02	2022	02	61.248653
20	Calera de Tango	2022-03	2022	03	63.495775
21	Calera de Tango	2022-04	2022	04	82.148590
22	Calera de Tango	2022-05	2022	05	91.139744
23	Calera de Tango	2022-06	2022	06	96.615526
24	Calera de Tango	2022-07	2022	07	92.587973
25	Calera de Tango	2022-08	2022	08	92.229715
26	Calera de Tango	2022-09	2022	09	98.041777
27	Cerrillos	2022-01	2022	01	99.046030
28	Cerrillos	2022-02	2022	02	89.873320
29	Cerrillos	2022-03	2022	03	86.588778

	comuna	delitos	AM
0	Santiago	3109	2022-01
9	La Florida	1149	2022-01
18	Maipú	1186	2022-01
32	Puente Alto	1585	2022-01
38	San Bernardo	1118	2022-01
52	Santiago	3025	2022-02
61	La Florida	1037	2022-02
70	Maipú	1130	2022-02
84	Puente Alto	1320	2022-02
90	San Bernardo	939	2022-02
104	Santiago	3829	2022-03
113	La Florida	1278	2022-03
122	Maipú	1498	2022-03
136	Puente Alto	1742	2022-03
142	San Bernardo	1234	2022-03
156	Santiago	3059	2022-04
165	La Florida	1255	2022-04
174	Maipú	1489	2022-04
188	Puente Alto	1644	2022-04
194	San Bernardo	1147	2022-04
208	Santiago	3150	2022-05
217	La Florida	1304	2022-05
226	Maipú	1515	2022-05
240	Puente Alto	1619	2022-05
246	San Bernardo	1292	2022-05
260	Santiago	2833	2022-06
269	La Florida	1259	2022-06
278	Maipú	1470	2022-06
292	Puente Alto	1563	2022-06
298	San Bernardo	1192	2022-06

	AM	delitos
0	2022-01	1629.4
1	2022-02	1490.2
2	2022-03	1916.2
3	2022-04	1718.8
4	2022-05	1776.0
5	2022-06	1663.4

	AM	avg	delitos
0	2022-01	86.770881	485.288462
1	2022-02	78.939598	430.884615
2	2022-03	80.486502	559.057692
3	2022-04	90.400395	514.038462
4	2022-05	92.816277	535.980769
5	2022-06	93.884878	503.307692

	comuna	sitio	Latitud	Longitud	avg	geometry
0	Alhué	ALHUE	-71.10167	-34.02681	98.090274	POINT (-71.10167 -34.02681)
1	Buin	ALTJA	-70.72444	-33.73297	93.811312	POINT (-70.72444 -33.73297)
2	Buin	BMATT	-70.74733	-33.70553	98.665796	POINT (-70.74733 -33.70553)
3	Buin	BUCDE	-70.73953	-33.73665	98.086915	POINT (-70.73953 -33.73665)
4	Buin	BUIC1	-70.73499	-33.73320	99.072326	POINT (-70.73499 -33.73320)
...	...	...	...	...	...	...
2099	Ñuñoa	SBOC2	-70.58649	-33.44663	97.414073	POINT (-70.58649 -33.44663)
2100	Ñuñoa	SEUGF	-70.62056	-33.46314	99.960154	POINT (-70.62056 -33.46314)
2101	Ñuñoa	SUCC1	-70.59948	-33.44821	99.998650	POINT (-70.59948 -33.44821)
2102	Ñuñoa	SUPDV	-70.60684	-33.44568	90.875267	POINT (-70.60684 -33.44568)
2103	Ñuñoa	TCDEP	-70.60982	-33.45704	97.463588	POINT (-70.60982 -33.45704)

Proyecto Visualización de Datos Javier Vega - Mg. DS UDD¶